{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a45ae9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2022-2023-02\n",
    "\n",
    "Python Data Analysis Course(PDAC)\n",
    "\n",
    "主讲：周圳\n",
    "\n",
    "week05:groupby 聚合运算"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84b5d5c6",
   "metadata": {},
   "source": [
    "# 上周内容\n",
    "# 模块储备\n",
    "* unique\n",
    "* nunique\n",
    "* value_counts\n",
    "# 数据开启\n",
    "* df.head()\n",
    "* df.tail()\n",
    "* df.info()\n",
    "# 数据目标\n",
    "* 字段（分类）\n",
    "* 数值（计算）\n",
    "* 时间（索引）"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "86e9986a",
   "metadata": {},
   "source": [
    "# 分组模式及其对象\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ef21d8",
   "metadata": {},
   "source": [
    "#### 1. 分组的一般模式\n",
    "* 依据 性别 分组，统计全国人口 寿命 的 平均值\n",
    "* 依据 季节 分组，对每一个季节的 温度 进行 组内标准化 1.依据 班级 分组，筛选出组内 数学分数 的 平均值超过80分的班级"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be5073a",
   "metadata": {},
   "source": [
    "1. Groupby 实践"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a69a8853",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "078a2399",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/learn_pandas.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97b92a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby('Gender')['Height'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d82e821",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(['School', 'Gender'])['Height'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b11469e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby([ 'Gender','School'])['Height'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba98d4da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86185\\AppData\\Local\\Temp\\ipykernel_18188\\3601636554.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_hurun['价值（亿元人民币）'] = df_hurun['价值（亿元人民币）'].astype('int64')\n"
     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>排名变化</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>价值（亿元人民币）</th>\n",
       "      <th>价值变化（亿元人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>抖音</td>\n",
       "      <td>13400</td>\n",
       "      <td>-10050</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>社交媒体</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>SpaceX</td>\n",
       "      <td>8400</td>\n",
       "      <td>1680</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>航天</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>蚂蚁集团</td>\n",
       "      <td>8000</td>\n",
       "      <td>-2010</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Stripe</td>\n",
       "      <td>4100</td>\n",
       "      <td>-2210</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>Shein</td>\n",
       "      <td>4000</td>\n",
       "      <td>2680</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>95</td>\n",
       "      <td>-16</td>\n",
       "      <td>Impossible 食品</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "      <td>美国</td>\n",
       "      <td>雷德伍德城</td>\n",
       "      <td>食品饮料</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>95</td>\n",
       "      <td>-16</td>\n",
       "      <td>微医</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>健康科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>58</td>\n",
       "      <td>蜂巢能源</td>\n",
       "      <td>460</td>\n",
       "      <td>190</td>\n",
       "      <td>中国</td>\n",
       "      <td>常州</td>\n",
       "      <td>新能源</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>99</td>\n",
       "      <td>-6</td>\n",
       "      <td>Better.com</td>\n",
       "      <td>460</td>\n",
       "      <td>60</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>99</td>\n",
       "      <td>-20</td>\n",
       "      <td>Automation Anywhere</td>\n",
       "      <td>460</td>\n",
       "      <td>-10</td>\n",
       "      <td>美国</td>\n",
       "      <td>圣何塞</td>\n",
       "      <td>人工智能</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     排名 排名变化                 企业名称  价值（亿元人民币） 价值变化（亿元人民币）  国家     城市    行业\n",
       "1     1    0                   抖音      13400      -10050  中国     北京  社交媒体\n",
       "2     2    1               SpaceX       8400        1680  美国    洛杉矶    航天\n",
       "3     3   -1                 蚂蚁集团       8000       -2010  中国     杭州  金融科技\n",
       "4     4    0               Stripe       4100       -2210  美国    旧金山  金融科技\n",
       "5     5   11                Shein       4000        2680  中国     广州  电子商务\n",
       "..   ..  ...                  ...        ...         ...  ..    ...   ...\n",
       "97   95  -16        Impossible 食品        470           0  美国  雷德伍德城  食品饮料\n",
       "98   95  -16                   微医        470           0  中国     杭州  健康科技\n",
       "99   99   58                 蜂巢能源        460         190  中国     常州   新能源\n",
       "100  99   -6           Better.com        460          60  美国     纽约  金融科技\n",
       "101  99  -20  Automation Anywhere        460         -10  美国    圣何塞  人工智能\n",
       "\n",
       "[101 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hurun_独角兽 = pd.read_html('https://www.hurun.net/zh-CN/Info/Detail?num=L9SQPH9FKJB1')[-3]\n",
    "hurun_独角兽[0:1].values.tolist()[0]\n",
    "df_hurun = hurun_独角兽[1:]\n",
    "df_hurun.columns = hurun_独角兽[0:1].values.tolist()[0]\n",
    "df_hurun['价值（亿元人民币）'] = df_hurun['价值（亿元人民币）'].astype('int64')\n",
    "df_hurun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5debd76",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hurun.to_excel('output_hurun.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e8784cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>价值（亿元人民币）</th>\n",
       "      <th>行业</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"17\" valign=\"top\">中国</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>570</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>企业服务</th>\n",
       "      <td>515</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>保险</th>\n",
       "      <td>740</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>1510</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>965</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>数字科技</th>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源</th>\n",
       "      <td>1770</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <td>670</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>3170</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>5340</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>社交媒体</th>\n",
       "      <td>13400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件服务</th>\n",
       "      <td>1870</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>10200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>食品饮料</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>以色列</th>\n",
       "      <th>区块链</th>\n",
       "      <td>535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">印度</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快递</th>\n",
       "      <td>720</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">印度尼西亚</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>土耳其</th>\n",
       "      <th>快递</th>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>墨西哥</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>580</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴哈马</th>\n",
       "      <th>区块链</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <th>软件服务</th>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <th>软件服务</th>\n",
       "      <td>1750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">瑞典</th>\n",
       "      <th>新能源</th>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <th>区块链</th>\n",
       "      <td>575</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">美国</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>2990</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>企业服务</th>\n",
       "      <td>1170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>1310</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>分析</th>\n",
       "      <td>575</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>1970</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快递</th>\n",
       "      <td>2320</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>575</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>1735</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生物科技</th>\n",
       "      <td>1340</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>1330</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>社交媒体</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网络安全</th>\n",
       "      <td>1690</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>航天</th>\n",
       "      <td>8400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件服务</th>\n",
       "      <td>4430</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>12335</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>食品饮料</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">英国</th>\n",
       "      <th>区块链</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件服务</th>\n",
       "      <td>1090</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融科技</th>\n",
       "      <td>4785</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>越南</th>\n",
       "      <th>消费品</th>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">韩国</th>\n",
       "      <th>区块链</th>\n",
       "      <td>535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>区块链</th>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             价值（亿元人民币）  行业\n",
       "国家    行业                  \n",
       "中国    人工智能         570   1\n",
       "      企业服务         515   1\n",
       "      保险           740   1\n",
       "      健康科技        1510   2\n",
       "      共享经济         965   1\n",
       "      大数据          535   1\n",
       "      数字科技        2000   1\n",
       "      新能源         1770   3\n",
       "      新能源汽车        600   1\n",
       "      新零售          670   1\n",
       "      机器人         1200   1\n",
       "      物流          3170   3\n",
       "      电子商务        5340   3\n",
       "      社交媒体       13400   1\n",
       "      软件服务        1870   2\n",
       "      金融科技       10200   2\n",
       "      食品饮料        1000   1\n",
       "以色列   区块链          535   1\n",
       "印度    共享经济         480   1\n",
       "      快递           720   1\n",
       "      教育科技        1500   1\n",
       "      游戏           535   1\n",
       "印度尼西亚 共享经济         700   1\n",
       "      电子商务        1300   1\n",
       "土耳其   快递           800   1\n",
       "墨西哥   电子商务         580   1\n",
       "巴哈马   区块链         1300   1\n",
       "德国    软件服务         555   1\n",
       "澳大利亚  软件服务        1750   1\n",
       "瑞典    新能源          800   1\n",
       "      新能源汽车       1300   1\n",
       "瑞士    区块链          575   1\n",
       "美国    人工智能        2990   5\n",
       "      企业服务        1170   1\n",
       "      健康科技        1310   2\n",
       "      共享经济        1000   1\n",
       "      分析           575   1\n",
       "      区块链         1970   3\n",
       "      大数据         2500   1\n",
       "      快递          2320   2\n",
       "      机器人          575   1\n",
       "      游戏           600   1\n",
       "      物流          1735   2\n",
       "      生物科技        1340   2\n",
       "      电子商务        1330   2\n",
       "      社交媒体        1000   1\n",
       "      网络安全        1690   3\n",
       "      航天          8400   1\n",
       "      软件服务        4430   8\n",
       "      金融科技       12335  11\n",
       "      食品饮料         470   1\n",
       "英国    区块链          700   1\n",
       "      软件服务        1090   2\n",
       "      金融科技        4785   4\n",
       "越南    消费品          550   1\n",
       "韩国    区块链          535   1\n",
       "      电子商务         560   1\n",
       "马耳他   区块链         3000   1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_国家_行业 = df_hurun.groupby(['国家','行业']).agg({'价值（亿元人民币）':sum,'行业':\"count\"})\n",
    "df_国家_行业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b5a0023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>价值（亿元人民币）</th>\n",
       "      <th>行业</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">人工智能</th>\n",
       "      <th>中国</th>\n",
       "      <td>570</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>2990</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">企业服务</th>\n",
       "      <th>中国</th>\n",
       "      <td>515</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>1170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>保险</th>\n",
       "      <th>中国</th>\n",
       "      <td>740</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">健康科技</th>\n",
       "      <th>中国</th>\n",
       "      <td>1510</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>1310</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">共享经济</th>\n",
       "      <th>中国</th>\n",
       "      <td>965</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度</th>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>分析</th>\n",
       "      <th>美国</th>\n",
       "      <td>575</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">区块链</th>\n",
       "      <th>以色列</th>\n",
       "      <td>535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>巴哈马</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞士</th>\n",
       "      <td>575</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>1970</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">大数据</th>\n",
       "      <th>中国</th>\n",
       "      <td>535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">快递</th>\n",
       "      <th>印度</th>\n",
       "      <td>720</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>土耳其</th>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>2320</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <th>印度</th>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>数字科技</th>\n",
       "      <th>中国</th>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">新能源</th>\n",
       "      <th>中国</th>\n",
       "      <td>1770</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">新能源汽车</th>\n",
       "      <th>中国</th>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <th>中国</th>\n",
       "      <td>670</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">机器人</th>\n",
       "      <th>中国</th>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>575</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>消费品</th>\n",
       "      <th>越南</th>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">游戏</th>\n",
       "      <th>印度</th>\n",
       "      <td>535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">物流</th>\n",
       "      <th>中国</th>\n",
       "      <td>3170</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>1735</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生物科技</th>\n",
       "      <th>美国</th>\n",
       "      <td>1340</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">电子商务</th>\n",
       "      <th>中国</th>\n",
       "      <td>5340</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>墨西哥</th>\n",
       "      <td>580</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>1330</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">社交媒体</th>\n",
       "      <th>中国</th>\n",
       "      <td>13400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网络安全</th>\n",
       "      <th>美国</th>\n",
       "      <td>1690</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>航天</th>\n",
       "      <th>美国</th>\n",
       "      <td>8400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">软件服务</th>\n",
       "      <th>中国</th>\n",
       "      <td>1870</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>1750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>4430</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>1090</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">金融科技</th>\n",
       "      <th>中国</th>\n",
       "      <td>10200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>12335</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>4785</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">食品饮料</th>\n",
       "      <th>中国</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             价值（亿元人民币）  行业\n",
       "行业    国家                  \n",
       "人工智能  中国           570   1\n",
       "      美国          2990   5\n",
       "企业服务  中国           515   1\n",
       "      美国          1170   1\n",
       "保险    中国           740   1\n",
       "健康科技  中国          1510   2\n",
       "      美国          1310   2\n",
       "共享经济  中国           965   1\n",
       "      印度           480   1\n",
       "      印度尼西亚        700   1\n",
       "      美国          1000   1\n",
       "分析    美国           575   1\n",
       "区块链   以色列          535   1\n",
       "      巴哈马         1300   1\n",
       "      瑞士           575   1\n",
       "      美国          1970   3\n",
       "      英国           700   1\n",
       "      韩国           535   1\n",
       "      马耳他         3000   1\n",
       "大数据   中国           535   1\n",
       "      美国          2500   1\n",
       "快递    印度           720   1\n",
       "      土耳其          800   1\n",
       "      美国          2320   2\n",
       "教育科技  印度          1500   1\n",
       "数字科技  中国          2000   1\n",
       "新能源   中国          1770   3\n",
       "      瑞典           800   1\n",
       "新能源汽车 中国           600   1\n",
       "      瑞典          1300   1\n",
       "新零售   中国           670   1\n",
       "机器人   中国          1200   1\n",
       "      美国           575   1\n",
       "消费品   越南           550   1\n",
       "游戏    印度           535   1\n",
       "      美国           600   1\n",
       "物流    中国          3170   3\n",
       "      美国          1735   2\n",
       "生物科技  美国          1340   2\n",
       "电子商务  中国          5340   3\n",
       "      印度尼西亚       1300   1\n",
       "      墨西哥          580   1\n",
       "      美国          1330   2\n",
       "      韩国           560   1\n",
       "社交媒体  中国         13400   1\n",
       "      美国          1000   1\n",
       "网络安全  美国          1690   3\n",
       "航天    美国          8400   1\n",
       "软件服务  中国          1870   2\n",
       "      德国           555   1\n",
       "      澳大利亚        1750   1\n",
       "      美国          4430   8\n",
       "      英国          1090   2\n",
       "金融科技  中国         10200   2\n",
       "      美国         12335  11\n",
       "      英国          4785   4\n",
       "食品饮料  中国          1000   1\n",
       "      美国           470   1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_行业_国家 = df_hurun.groupby(['行业','国家']).agg({'价值（亿元人民币）':sum,'行业':'count'})\n",
    "df_行业_国家"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7af31b16",
   "metadata": {},
   "source": [
    "# 2. Groupby对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "affb3766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000172E9731550>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df_hurun.groupby(['行业','国家'])\n",
    "gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f35d6f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.ngroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "595f278c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([('人工智能', '中国'), ('人工智能', '美国'), ('企业服务', '中国'), ('企业服务', '美国'), ('保险', '中国'), ('健康科技', '中国'), ('健康科技', '美国'), ('共享经济', '中国'), ('共享经济', '印度'), ('共享经济', '印度尼西亚'), ('共享经济', '美国'), ('分析', '美国'), ('区块链', '以色列'), ('区块链', '巴哈马'), ('区块链', '瑞士'), ('区块链', '美国'), ('区块链', '英国'), ('区块链', '韩国'), ('区块链', '马耳他'), ('大数据', '中国'), ('大数据', '美国'), ('快递', '印度'), ('快递', '土耳其'), ('快递', '美国'), ('教育科技', '印度'), ('数字科技', '中国'), ('新能源', '中国'), ('新能源', '瑞典'), ('新能源汽车', '中国'), ('新能源汽车', '瑞典'), ('新零售', '中国'), ('机器人', '中国'), ('机器人', '美国'), ('消费品', '越南'), ('游戏', '印度'), ('游戏', '美国'), ('物流', '中国'), ('物流', '美国'), ('生物科技', '美国'), ('电子商务', '中国'), ('电子商务', '印度尼西亚'), ('电子商务', '墨西哥'), ('电子商务', '美国'), ('电子商务', '韩国'), ('社交媒体', '中国'), ('社交媒体', '美国'), ('网络安全', '美国'), ('航天', '美国'), ('软件服务', '中国'), ('软件服务', '德国'), ('软件服务', '澳大利亚'), ('软件服务', '美国'), ('软件服务', '英国'), ('金融科技', '中国'), ('金融科技', '美国'), ('金融科技', '英国'), ('食品饮料', '中国'), ('食品饮料', '美国')])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = gb.groups\n",
    "res.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3305d976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "行业     国家   \n",
       "人工智能   中国        1\n",
       "       美国        5\n",
       "企业服务   中国        1\n",
       "       美国        1\n",
       "保险     中国        1\n",
       "健康科技   中国        2\n",
       "       美国        2\n",
       "共享经济   中国        1\n",
       "       印度        1\n",
       "       印度尼西亚     1\n",
       "       美国        1\n",
       "分析     美国        1\n",
       "区块链    以色列       1\n",
       "       巴哈马       1\n",
       "       瑞士        1\n",
       "       美国        3\n",
       "       英国        1\n",
       "       韩国        1\n",
       "       马耳他       1\n",
       "大数据    中国        1\n",
       "       美国        1\n",
       "快递     印度        1\n",
       "       土耳其       1\n",
       "       美国        2\n",
       "教育科技   印度        1\n",
       "数字科技   中国        1\n",
       "新能源    中国        3\n",
       "       瑞典        1\n",
       "新能源汽车  中国        1\n",
       "       瑞典        1\n",
       "新零售    中国        1\n",
       "机器人    中国        1\n",
       "       美国        1\n",
       "消费品    越南        1\n",
       "游戏     印度        1\n",
       "       美国        1\n",
       "物流     中国        3\n",
       "       美国        2\n",
       "生物科技   美国        2\n",
       "电子商务   中国        3\n",
       "       印度尼西亚     1\n",
       "       墨西哥       1\n",
       "       美国        2\n",
       "       韩国        1\n",
       "社交媒体   中国        1\n",
       "       美国        1\n",
       "网络安全   美国        3\n",
       "航天     美国        1\n",
       "软件服务   中国        2\n",
       "       德国        1\n",
       "       澳大利亚      1\n",
       "       美国        8\n",
       "       英国        2\n",
       "金融科技   中国        2\n",
       "       美国       11\n",
       "       英国        4\n",
       "食品饮料   中国        1\n",
       "       美国        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbbfb22",
   "metadata": {},
   "source": [
    "  # 3. 聚合函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0fdfe6e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>价值（亿元人民币）</th>\n",
       "      <th>行业</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">人工智能</th>\n",
       "      <th>哈里斯堡</th>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>圣何塞</th>\n",
       "      <td>460</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>570</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>旧金山</th>\n",
       "      <td>2030</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>企业服务</th>\n",
       "      <th>宿迁</th>\n",
       "      <td>515</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">金融科技</th>\n",
       "      <th>深圳</th>\n",
       "      <td>2200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>纽约</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芝加哥</th>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">食品饮料</th>\n",
       "      <th>北京</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>雷德伍德城</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            价值（亿元人民币）  行业\n",
       "行业   城市                  \n",
       "人工智能 哈里斯堡         500   1\n",
       "     圣何塞          460   1\n",
       "     广州           570   1\n",
       "     旧金山         2030   3\n",
       "企业服务 宿迁           515   1\n",
       "...               ...  ..\n",
       "金融科技 深圳          2200   1\n",
       "     纽约          1000   2\n",
       "     芝加哥         1500   1\n",
       "食品饮料 北京          1000   1\n",
       "     雷德伍德城        470   1\n",
       "\n",
       "[82 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_行业_城市 = df_hurun.groupby(['行业','城市']).agg({'价值（亿元人民币）':sum,'行业':'count'})\n",
    "df_行业_城市"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c051d8",
   "metadata": {},
   "source": [
    "## 3.1聚合函数的重命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5b9793b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>价值_亿</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">人工智能</th>\n",
       "      <th>哈里斯堡</th>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>圣何塞</th>\n",
       "      <td>460</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>570</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>旧金山</th>\n",
       "      <td>2030</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>企业服务</th>\n",
       "      <th>宿迁</th>\n",
       "      <td>515</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">金融科技</th>\n",
       "      <th>深圳</th>\n",
       "      <td>2200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>纽约</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芝加哥</th>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">食品饮料</th>\n",
       "      <th>北京</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>雷德伍德城</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            价值_亿  企业数量\n",
       "行业   城市               \n",
       "人工智能 哈里斯堡    500     1\n",
       "     圣何塞     460     1\n",
       "     广州      570     1\n",
       "     旧金山    2030     3\n",
       "企业服务 宿迁      515     1\n",
       "...          ...   ...\n",
       "金融科技 深圳     2200     1\n",
       "     纽约     1000     2\n",
       "     芝加哥    1500     1\n",
       "食品饮料 北京     1000     1\n",
       "     雷德伍德城   470     1\n",
       "\n",
       "[82 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_行业_城市_重命名 = df_hurun.groupby(['行业','城市']).agg(价值_亿 = ('价值（亿元人民币）','sum') ,企业数量 = ('行业',\"count\"))\n",
    "df_行业_城市_重命名"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ab832e",
   "metadata": {},
   "source": [
    "# 4. 正确的输出数据分析结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1120a82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.ExcelWriter('output_hurun_analysis_results.xlsx') as writer:  \n",
    "    df_国家_行业.to_excel(writer, sheet_name='分国家行业')\n",
    "    df_行业_国家.to_excel(writer, sheet_name='分行业国家')\n",
    "    df_行业_城市.to_excel(writer, sheet_name='分行业城市')\n",
    "    df_行业_城市_重命名.to_excel(writer, sheet_name='分行业城市_重命名')"
   ]
  }
 ],
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